Chapter 6 Diversity analysis
6.1 Alpha diversity
# Calculate Hill numbers
richness <- genome_counts_filt %>%
column_to_rownames(var = "genome") %>%
dplyr::select(where(~ !all(. == 0))) %>%
hilldiv(., q = 0) %>%
t() %>%
as.data.frame() %>%
dplyr::rename(richness = 1) %>%
rownames_to_column(var = "sample")
neutral <- genome_counts_filt %>%
column_to_rownames(var = "genome") %>%
dplyr::select(where(~ !all(. == 0))) %>%
hilldiv(., q = 1) %>%
t() %>%
as.data.frame() %>%
dplyr::rename(neutral = 1) %>%
rownames_to_column(var = "sample")
phylogenetic <- genome_counts_filt %>%
column_to_rownames(var = "genome") %>%
dplyr::select(where(~ !all(. == 0))) %>%
hilldiv(., q = 1, tree = genome_tree) %>%
t() %>%
as.data.frame() %>%
dplyr::rename(phylogenetic = 1) %>%
rownames_to_column(var = "sample")
# Aggregate basal GIFT into elements
dist <- genome_gifts %>%
to.elements(., GIFT_db3) %>%
traits2dist(., method = "gower")
functional <- genome_counts_filt %>%
column_to_rownames(var = "genome") %>%
dplyr::select(where(~ !all(. == 0))) %>%
hilldiv(., q = 1, dist = dist) %>%
t() %>%
as.data.frame() %>%
dplyr::rename(functional = 1) %>%
rownames_to_column(var = "sample") %>%
mutate(functional = if_else(is.nan(functional), 1, functional))
# Merge all metrics
alpha_div <- richness %>%
full_join(neutral, by = join_by(sample == sample)) %>%
full_join(phylogenetic, by = join_by(sample == sample)) %>%
full_join(functional, by = join_by(sample == sample))6.1.1 Wild samples
alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="0_Wild") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
ggplot(aes(y = value, x = Population, group=Population, color=Population, fill=Population)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#008080', "#d57d2c")) +
scale_fill_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#00808050', "#d57d2c50")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.58) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")
6.1.2 Acclimation samples
alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="1_Acclimation") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
ggplot(aes(y = value, x = Population, group=Population, color=Population, fill=Population)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#008080', "#d57d2c")) +
scale_fill_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#00808050', "#d57d2c50")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.58) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")
6.1.3 Antibiotics samples
alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="2_Antibiotics") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
ggplot(aes(y = value, x = Population, group=Population, color=Population, fill=Population)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#008080', "#d57d2c")) +
scale_fill_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#00808050', "#d57d2c50")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.58) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")
6.1.4 Transplant_1 samples
alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="3_Transplant1") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c", "#76b183")) +
scale_fill_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA50","#d57d2c50","#76b18350")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.7) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")
6.1.5 Transplant_2 samples
alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="4_Transplant2") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c", "#76b183")) +
scale_fill_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA50","#d57d2c50","#76b18350")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.7) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")
6.1.6 Post-Transplant_1 samples
alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="5_Post-FMT1") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c", "#76b183")) +
scale_fill_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA50","#d57d2c50","#76b18350")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.7) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")
6.1.7 Post-Transplant_2 samples
alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="6_Post-FMT2") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-control","Warm-control", "Cold-intervention"),
values=c("#4477AA","#d57d2c", "#76b183")) +
scale_fill_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-control","Warm-control", "Cold-intervention"),
values=c("#4477AA50","#d57d2c50","#76b18350")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.7) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")
6.2 Beta diversity
beta_q0n <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
hillpair(., q = 0)
beta_q1n <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
hillpair(., q = 1)
beta_q1p <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
hillpair(., q = 1, tree = genome_tree)
beta_q1f <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
hillpair(., q = 1, dist = dist)6.3 Permanovas
6.3.1 1. Are the wild populations similar?
6.3.1.1 Wild: P.muralis vs P.liolepis
wild <- meta %>%
filter(time_point == "0_Wild")
# Create a temporary modified version of genome_counts_filt
temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL
wild.counts <- temp_genome_counts[, which(colnames(temp_genome_counts) %in% rownames(wild))]
identical(sort(colnames(wild.counts)), sort(as.character(rownames(wild))))
wild_nmds <- sample_metadata %>%
filter(time_point == "0_Wild")6.3.1.2 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.000012 0.000012 0.0012 999 0.976
Residuals 25 0.257281 0.010291
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.976
Hot_dry 0.97302 adonis2(formula=beta_div_richness_wild$S ~ Population, data=wild[labels(beta_div_richness_wild$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 1 | 1.542719 | 0.2095041 | 6.625717 | 0.001 |
| Residual | 25 | 5.820951 | 0.7904959 | NA | NA |
| Total | 26 | 7.363669 | 1.0000000 | NA | NA |
6.3.1.3 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.000048 0.0000476 0.0044 999 0.944
Residuals 25 0.270114 0.0108046
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.946
Hot_dry 0.94763 adonis2(formula=beta_div_neutral_wild$S ~ Population, data=wild[labels(beta_div_neutral_wild$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 1 | 1.918266 | 0.2608511 | 8.822682 | 0.001 |
| Residual | 25 | 5.435610 | 0.7391489 | NA | NA |
| Total | 26 | 7.353876 | 1.0000000 | NA | NA |
6.3.1.4 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.03585 0.035847 2.4912 999 0.118
Residuals 25 0.35973 0.014389
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.119
Hot_dry 0.12705 adonis2(formula=beta_div_phylo_wild$S ~ Population, data=wild[labels(beta_div_phylo_wild$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 1 | 0.3218613 | 0.2162815 | 6.899207 | 0.001 |
| Residual | 25 | 1.1662981 | 0.7837185 | NA | NA |
| Total | 26 | 1.4881594 | 1.0000000 | NA | NA |
6.3.1.5 Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.019387 0.019387 1.653 999 0.195
Residuals 25 0.293200 0.011728
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.206
Hot_dry 0.21033 adonis2(formula=beta_div_func_wild$S ~ Population, data=wild[labels(beta_div_func_wild$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 1 | 0.0831048 | 0.1680538 | 5.05002 | 0.051 |
| Residual | 25 | 0.4114083 | 0.8319462 | NA | NA |
| Total | 26 | 0.4945131 | 1.0000000 | NA | NA |
beta_q0n_nmds_wild <- beta_div_richness_wild$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(wild_nmds, by = join_by(sample == Tube_code))
beta_q1n_nmds_wild <- beta_div_neutral_wild$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(wild_nmds, by = join_by(sample == Tube_code))
beta_q1p_nmds_wild <- beta_div_phylo_wild$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(wild_nmds, by = join_by(sample == Tube_code))
beta_q1f_nmds_wild <- beta_div_func_wild$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(wild_nmds, by = join_by(sample == Tube_code))
6.3.2 2. Effect of acclimation
accli <- meta %>%
filter(time_point == "1_Acclimation")
# Create a temporary modified version of genome_counts_filt
temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL
accli.counts <- temp_genome_counts[, which(colnames(temp_genome_counts) %in% rownames(accli))]
identical(sort(colnames(accli.counts)), sort(as.character(rownames(accli))))
accli_nmds <- sample_metadata %>%
filter(time_point == "1_Acclimation")6.3.2.1 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.11796 0.117959 12.963 999 0.002 **
Residuals 25 0.22748 0.009099
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.001
Hot_dry 0.0013711 adonis2(formula=beta_div_richness_accli$S ~ Population, data=accli[labels(beta_div_richness_accli$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 1 | 1.639807 | 0.179834 | 5.481634 | 0.001 |
| Residual | 25 | 7.478640 | 0.820166 | NA | NA |
| Total | 26 | 9.118447 | 1.000000 | NA | NA |
6.3.2.2 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.07844 0.078443 5.2384 999 0.036 *
Residuals 25 0.37437 0.014975
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.033
Hot_dry 0.030815 adonis2(formula=beta_div_neutral_accli$S ~ Population, data=accli[labels(beta_div_neutral_accli$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 1 | 1.947003 | 0.2306127 | 7.493387 | 0.001 |
| Residual | 25 | 6.495736 | 0.7693873 | NA | NA |
| Total | 26 | 8.442739 | 1.0000000 | NA | NA |
6.3.2.3 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.06739 0.067395 2.9532 999 0.1
Residuals 25 0.57052 0.022821
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.096
Hot_dry 0.098068 adonis2(formula=beta_div_phylo_accli$S ~ Population, data=accli[labels(beta_div_phylo_accli$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 1 | 0.2441653 | 0.1224638 | 3.488854 | 0.015 |
| Residual | 25 | 1.7496100 | 0.8775362 | NA | NA |
| Total | 26 | 1.9937754 | 1.0000000 | NA | NA |
6.3.2.4 Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.02351 0.023513 0.635 999 0.447
Residuals 25 0.92569 0.037028
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.458
Hot_dry 0.43303 adonis2(formula=beta_div_func_accli$S ~ Population, data=accli[labels(beta_div_func_accli$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 1 | 0.0279416 | 0.024809 | 0.6360037 | 0.446 |
| Residual | 25 | 1.0983283 | 0.975191 | NA | NA |
| Total | 26 | 1.1262699 | 1.000000 | NA | NA |
beta_q0n_nmds_accli <- beta_div_richness_accli$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(accli_nmds, by = join_by(sample == Tube_code))
beta_q1n_nmds_accli <- beta_div_neutral_accli$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(accli_nmds, by = join_by(sample == Tube_code))
beta_q1p_nmds_accli <- beta_div_phylo_accli$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(accli_nmds, by = join_by(sample == Tube_code))
beta_q1f_nmds_accli <- beta_div_func_accli$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(accli_nmds, by = join_by(sample == Tube_code))
6.3.3 3. Comparison between Wild and Acclimation
accli1 <- meta %>%
filter(time_point == "0_Wild" | time_point == "1_Acclimation")
temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL
accli1.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(accli1))]
identical(sort(colnames(accli1.counts)),sort(as.character(rownames(accli1))))
accli1_nmds <- sample_metadata %>%
filter(time_point == "0_Wild" | time_point == "1_Acclimation")6.3.3.0.1 Number of samples used
[1] 546.3.3.0.2 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.05014 0.050145 6.2252 999 0.015 *
Residuals 52 0.41886 0.008055
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
0_Wild 1_Acclimation
0_Wild 0.014
1_Acclimation 0.015808 adonis2(formula=beta_div_richness_accli1$S ~ time_point*Population, data=accli1[labels(beta_div_richness_accli1$S),], permutations=999, strata=accli1$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 3.799791 | 0.222218 | 4.761789 | 0.003 |
| Residual | 50 | 13.299591 | 0.777782 | NA | NA |
| Total | 53 | 17.099381 | 1.000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_richness_accli1$S,accli1_arrange$Population_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Cold_wet.1_Acclimation vs Hot_dry.1_Acclimation | 1 | 1.6398067 | 5.481634 | 0.17983399 | 0.001 | 0.006 | * |
| Cold_wet.1_Acclimation vs Cold_wet.0_Wild | 1 | 0.5861897 | 2.045106 | 0.05673741 | 0.002 | 0.012 | . |
| Cold_wet.1_Acclimation vs Hot_dry.0_Wild | 1 | 1.5524421 | 4.967864 | 0.16577304 | 0.001 | 0.006 | * |
| Hot_dry.1_Acclimation vs Cold_wet.0_Wild | 1 | 1.8259388 | 8.319131 | 0.24968031 | 0.001 | 0.006 | * |
| Hot_dry.1_Acclimation vs Hot_dry.0_Wild | 1 | 0.3856333 | 1.736034 | 0.09788177 | 0.008 | 0.048 | . |
| Cold_wet.0_Wild vs Hot_dry.0_Wild | 1 | 1.5427188 | 6.625717 | 0.20950408 | 0.001 | 0.006 | * |
6.3.3.0.3 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.0199 0.0199035 2.1213 999 0.138
Residuals 52 0.4879 0.0093827
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
0_Wild 1_Acclimation
0_Wild 0.135
1_Acclimation 0.15128 adonis2(formula=beta_div_neutral_accli1$S ~ time_point*Population, data=accli1[labels(beta_div_neutral_accli1$S),], permutations=999, strata=accli1$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 4.770321 | 0.2856195 | 6.663569 | 0.001 |
| Residual | 50 | 11.931346 | 0.7143805 | NA | NA |
| Total | 53 | 16.701667 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_neutral_accli1$S,accli1_arrange$Population_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Cold_wet.1_Acclimation vs Hot_dry.1_Acclimation | 1 | 1.9470028 | 7.493387 | 0.23061269 | 0.001 | 0.006 | * |
| Cold_wet.1_Acclimation vs Cold_wet.0_Wild | 1 | 0.8152517 | 3.211984 | 0.08631584 | 0.001 | 0.006 | * |
| Cold_wet.1_Acclimation vs Hot_dry.0_Wild | 1 | 2.1302445 | 7.742971 | 0.23647735 | 0.001 | 0.006 | * |
| Hot_dry.1_Acclimation vs Cold_wet.0_Wild | 1 | 2.0371666 | 10.078295 | 0.28730857 | 0.001 | 0.006 | * |
| Hot_dry.1_Acclimation vs Hot_dry.0_Wild | 1 | 0.6314393 | 3.060027 | 0.16054681 | 0.001 | 0.006 | * |
| Cold_wet.0_Wild vs Hot_dry.0_Wild | 1 | 1.9182663 | 8.822682 | 0.26085105 | 0.001 | 0.006 | * |
6.3.3.0.4 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.01334 0.013340 0.6524 999 0.43
Residuals 52 1.06332 0.020449
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
0_Wild 1_Acclimation
0_Wild 0.426
1_Acclimation 0.42294 adonis2(formula=beta_div_phylo_accli1$S ~ time_point*Population, data=accli1[labels(beta_div_phylo_accli1$S),], permutations=999, strata=accli1$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 0.855070 | 0.2267502 | 4.887385 | 0.001 |
| Residual | 50 | 2.915908 | 0.7732498 | NA | NA |
| Total | 53 | 3.770978 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_phylo_accli1$S,accli1_arrange$Population_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Cold_wet.1_Acclimation vs Hot_dry.1_Acclimation | 1 | 0.2441653 | 3.488854 | 0.1224638 | 0.021 | 0.126 | |
| Cold_wet.1_Acclimation vs Cold_wet.0_Wild | 1 | 0.2949378 | 4.881474 | 0.1255475 | 0.003 | 0.018 | . |
| Cold_wet.1_Acclimation vs Hot_dry.0_Wild | 1 | 0.3992196 | 4.442501 | 0.1508874 | 0.004 | 0.024 | . |
| Hot_dry.1_Acclimation vs Cold_wet.0_Wild | 1 | 0.2121479 | 7.924059 | 0.2406769 | 0.001 | 0.006 | * |
| Hot_dry.1_Acclimation vs Hot_dry.0_Wild | 1 | 0.2092433 | 3.885515 | 0.1953942 | 0.001 | 0.006 | * |
| Cold_wet.0_Wild vs Hot_dry.0_Wild | 1 | 0.3218613 | 6.899207 | 0.2162815 | 0.001 | 0.006 | * |
6.3.3.0.5 Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.01264 0.012640 0.4951 999 0.486
Residuals 52 1.32764 0.025532
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
0_Wild 1_Acclimation
0_Wild 0.481
1_Acclimation 0.4848 adonis2(formula=beta_div_func_accli1$S ~ time_point*Population, data=accli1[labels(beta_div_func_accli1$S),], permutations=999, strata=accli1$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 0.1558147 | 0.0935514 | 1.720109 | 0.332 |
| Residual | 50 | 1.5097366 | 0.9064486 | NA | NA |
| Total | 53 | 1.6655513 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_func_accli1$S,accli1_arrange$Population_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Cold_wet.1_Acclimation vs Hot_dry.1_Acclimation | 1 | 0.027941631 | 0.6360037 | 0.02480900 | 0.441 | 1.000 | |
| Cold_wet.1_Acclimation vs Cold_wet.0_Wild | 1 | 0.018947902 | 0.4777313 | 0.01385623 | 0.490 | 1.000 | |
| Cold_wet.1_Acclimation vs Hot_dry.0_Wild | 1 | 0.145480076 | 3.6909571 | 0.12864531 | 0.048 | 0.288 | |
| Hot_dry.1_Acclimation vs Cold_wet.0_Wild | 1 | 0.002523515 | 0.1203153 | 0.00478956 | 0.617 | 1.000 | |
| Hot_dry.1_Acclimation vs Hot_dry.0_Wild | 1 | 0.040973276 | 4.0663305 | 0.20264445 | 0.098 | 0.588 | |
| Cold_wet.0_Wild vs Hot_dry.0_Wild | 1 | 0.083104797 | 5.0500195 | 0.16805378 | 0.045 | 0.270 |
beta_richness_nmds_accli1 <- beta_div_richness_accli1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(accli1_nmds, by = c("sample" = "Tube_code"))
beta_neutral_nmds_accli1 <- beta_div_neutral_accli1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(accli1_nmds, by = c("sample" = "Tube_code"))
beta_phylo_nmds_accli1 <- beta_div_phylo_accli1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(accli1_nmds, by = join_by(sample == Tube_code))
beta_func_nmds_accli1 <- beta_div_func_accli1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(accli1_nmds, by = join_by(sample == Tube_code))
6.3.4 4. Do the antibiotics work?
6.3.4.1 Antibiotics
treat1 <- meta %>%
filter(time_point == "2_Antibiotics")
temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL
treat1.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(treat1))]
identical(sort(colnames(treat1.counts)),sort(as.character(rownames(treat1))))
treat1_nmds <- sample_metadata %>%
filter(time_point == "2_Antibiotics")6.3.4.1.2 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.015319 0.0153186 6.8764 999 0.016 *
Residuals 21 0.046782 0.0022277
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.007
Hot_dry 0.015919 adonis2(formula=beta_div_richness_treat1$S ~ Population, data=treat1[labels(beta_div_neutral_treat1$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 1 | 1.356644 | 0.1527052 | 3.784762 | 0.001 |
| Residual | 21 | 7.527429 | 0.8472948 | NA | NA |
| Total | 22 | 8.884073 | 1.0000000 | NA | NA |
6.3.4.1.3 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.030536 0.0305358 3.8593 999 0.052 .
Residuals 21 0.166158 0.0079123
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.061
Hot_dry 0.062842 adonis2(formula=beta_div_neutral_treat1$S ~ Population, data=treat1[labels(beta_div_neutral_treat1$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 1 | 1.785669 | 0.2085055 | 5.532084 | 0.001 |
| Residual | 21 | 6.778468 | 0.7914945 | NA | NA |
| Total | 22 | 8.564137 | 1.0000000 | NA | NA |
6.3.4.1.4 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.012041 0.012041 0.9898 999 0.324
Residuals 21 0.255459 0.012165
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.325
Hot_dry 0.33111 adonis2(formula=beta_div_phylo_treat1$S ~ Population, data=treat1[labels(beta_div_phylo_treat1$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 1 | 0.8963254 | 0.1888758 | 4.889993 | 0.001 |
| Residual | 21 | 3.8492558 | 0.8111242 | NA | NA |
| Total | 22 | 4.7455811 | 1.0000000 | NA | NA |
6.3.4.1.5 Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.01969 0.019691 0.4738 999 0.495
Residuals 21 0.87274 0.041559
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.505
Hot_dry 0.49877 adonis2(formula=beta_div_func_treat1$S ~ Population, data=treat1[labels(beta_div_func_treat1$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 1 | 0.0246208 | 0.0133549 | 0.2842492 | 0.679 |
| Residual | 21 | 1.8189576 | 0.9866451 | NA | NA |
| Total | 22 | 1.8435784 | 1.0000000 | NA | NA |
beta_richness_nmds_treat1 <- beta_div_richness_treat1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(treat1_nmds, by = c("sample" = "Tube_code"))
beta_neutral_nmds_treat1 <- beta_div_neutral_treat1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(treat1_nmds, by = c("sample" = "Tube_code"))
beta_phylo_nmds_treat1 <- beta_div_phylo_treat1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(treat1_nmds, by = join_by(sample == Tube_code))
beta_func_nmds_treat1 <- beta_div_func_treat1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(treat1_nmds, by = join_by(sample == Tube_code))
6.3.4.2 Acclimation vs antibiotics
treat <- meta %>%
filter(time_point == "1_Acclimation" | time_point == "2_Antibiotics")
temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL
treat.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(treat))]
identical(sort(colnames(treat.counts)),sort(as.character(rownames(treat))))
treat_nmds <- sample_metadata %>%
filter(time_point == "1_Acclimation" | time_point == "2_Antibiotics")6.3.4.2.2 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.025318 0.0253178 6.021 999 0.012 *
Residuals 48 0.201837 0.0042049
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 2_Antibiotics
1_Acclimation 0.022
2_Antibiotics 0.017817 adonis2(formula=beta_div_richness_treat$S ~ time_point*Population, data=treat[labels(beta_div_neutral_treat$S),], permutations=999,strata=treat$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 4.885035 | 0.2455889 | 4.991572 | 0.001 |
| Residual | 46 | 15.006068 | 0.7544111 | NA | NA |
| Total | 49 | 19.891103 | 1.0000000 | NA | NA |
6.3.4.2.3 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.039587 0.039587 6.8387 999 0.012 *
Residuals 48 0.277854 0.005789
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 2_Antibiotics
1_Acclimation 0.013
2_Antibiotics 0.011886 adonis2(formula=beta_div_neutral_treat$S ~ time_point*Population, data=treat[labels(beta_div_neutral_treat$S),], permutations=999,strata=treat$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 5.756853 | 0.3024978 | 6.649871 | 0.001 |
| Residual | 46 | 13.274204 | 0.6975022 | NA | NA |
| Total | 49 | 19.031057 | 1.0000000 | NA | NA |
6.3.4.2.4 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.58372 0.58372 35.413 999 0.001 ***
Residuals 48 0.79119 0.01648
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 2_Antibiotics
1_Acclimation 0.001
2_Antibiotics 2.9795e-07 adonis2(formula=beta_div_phylo_treat$S ~ time_point*Population, data=treat[labels(beta_div_phylo_treat$S),], permutations=999,strata=treat$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 2.947011 | 0.344846 | 8.070832 | 0.001 |
| Residual | 46 | 5.598866 | 0.655154 | NA | NA |
| Total | 49 | 8.545877 | 1.000000 | NA | NA |
6.3.4.2.5 Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.17618 0.17618 4.7941 999 0.035 *
Residuals 48 1.76400 0.03675
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 2_Antibiotics
1_Acclimation 0.032
2_Antibiotics 0.033451 adonis2(formula=beta_div_func_treat$S ~ time_point*Population, data=treat[labels(beta_div_func_treat$S),], permutations=999,strata=treat$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 1.795938 | 0.3810423 | 9.439497 | 0.001 |
| Residual | 46 | 2.917286 | 0.6189577 | NA | NA |
| Total | 49 | 4.713224 | 1.0000000 | NA | NA |
beta_richness_nmds_treat <- beta_div_richness_treat$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(treat_nmds, by = c("sample" = "Tube_code"))
beta_neutral_nmds_treat <- beta_div_neutral_treat$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(treat_nmds, by = c("sample" = "Tube_code"))
beta_phylo_nmds_treat <- beta_div_phylo_treat$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(treat_nmds, by = join_by(sample == Tube_code))
beta_func_nmds_treat <- beta_div_func_treat$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(treat_nmds, by = join_by(sample == Tube_code))
6.3.5 5. Does the FMT work?
6.3.5.1 Comparison between FMT2 vs Post-FMT2
#Create newID to identify duplicated samples
transplants_metadata<-sample_metadata%>%
mutate(Tube_code=str_remove_all(Tube_code, "_a"))
transplants_metadata$newID <- paste(transplants_metadata$Tube_code, "_", transplants_metadata$individual)
transplant3<-transplants_metadata%>%
filter(time_point == "4_Transplant2" | time_point == "6_Post-FMT2")%>%
column_to_rownames("newID")
transplant3_nmds <- transplants_metadata %>%
filter(time_point == "4_Transplant2" | time_point == "6_Post-FMT2")
full_counts<-temp_genome_counts %>%
t()%>%
as.data.frame()%>%
rownames_to_column("Tube_code")%>%
full_join(transplants_metadata,by = join_by(Tube_code == Tube_code))
transplant3_counts<-full_counts %>%
filter(time_point == "4_Transplant2" | time_point == "6_Post-FMT2") %>%
subset(select=-c(315:324)) %>%
column_to_rownames("newID")%>%
subset(select=-c(1))%>%
t() %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric)
identical(sort(colnames(transplant3_counts)),sort(as.character(rownames(transplant3))))6.3.5.1.1 Number of samples used
[1] 49beta_div_richness_transplant3<-hillpair(data=transplant3_counts, q=0)
beta_div_neutral_transplant3<-hillpair(data=transplant3_counts, q=1)
beta_div_phylo_transplant3<-hillpair(data=transplant3_counts, q=1, tree=genome_tree)
beta_div_func_transplant3<-hillpair(data=transplant3_counts, q=1, dist=dist)6.3.5.1.2 Richness
adonis2(formula=beta_div_richness_transplant3$S ~ Population+time_point+type, data=transplant3[labels(beta_div_richness_transplant3$S),], permutations=999, strata=transplant3$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 3.500812 | 0.2535872 | 5.096117 | 0.001 |
| Residual | 45 | 10.304350 | 0.7464128 | NA | NA |
| Total | 48 | 13.805162 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_richness_transplant3$S,transplant3_arrange$type, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control vs Treatment | 1 | 1.4169018 | 5.739828 | 0.15622903 | 0.001 | 0.003 | * |
| Control vs Hot_control | 1 | 2.0940966 | 8.509112 | 0.21005427 | 0.001 | 0.003 | * |
| Treatment vs Hot_control | 1 | 0.3004618 | 1.265034 | 0.04179854 | 0.155 | 0.465 |
6.3.5.1.3 Neutral
adonis2(formula=beta_div_neutral_transplant3$S ~ Population+time_point+type, data=transplant3[labels(beta_div_neutral_transplant3$S),], permutations=999, strata=transplant3$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 4.128749 | 0.3031142 | 6.524331 | 0.001 |
| Residual | 45 | 9.492350 | 0.6968858 | NA | NA |
| Total | 48 | 13.621099 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_neutral_transplant3$S,transplant3_arrange$type, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control vs Treatment | 1 | 1.8758788 | 8.282671 | 0.21084796 | 0.001 | 0.003 | * |
| Control vs Hot_control | 1 | 2.4396317 | 10.635546 | 0.24945256 | 0.001 | 0.003 | * |
| Treatment vs Hot_control | 1 | 0.3158428 | 1.394345 | 0.04587515 | 0.127 | 0.381 |
6.3.5.1.4 Phylogenetic
adonis2(formula=beta_div_phylo_transplant3$S ~ Population+time_point+type, data=transplant3[labels(beta_div_phylo_transplant3$S),], permutations=999, strata=transplant3$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 0.3971179 | 0.2701357 | 5.551766 | 0.001 |
| Residual | 45 | 1.0729504 | 0.7298643 | NA | NA |
| Total | 48 | 1.4700683 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_phylo_transplant3$S,transplant3_arrange$type, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control vs Treatment | 1 | 0.14387705 | 5.735321 | 0.15612552 | 0.001 | 0.003 | * |
| Control vs Hot_control | 1 | 0.22715701 | 9.044894 | 0.22036587 | 0.001 | 0.003 | * |
| Treatment vs Hot_control | 1 | 0.04648319 | 1.704277 | 0.05550617 | 0.126 | 0.378 |
6.3.5.1.5 Functional
adonis2(formula=beta_div_func_transplant3$S ~ Population+time_point+type, data=transplant3[labels(beta_div_func_transplant3$S),], permutations=999, strata=transplant3$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 0.0880056 | 0.0736928 | 1.193332 | 0.428 |
| Residual | 45 | 1.1062168 | 0.9263072 | NA | NA |
| Total | 48 | 1.1942224 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_func_transplant3$S,transplant3_arrange$type, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control vs Treatment | 1 | 0.08177408 | 4.84137651 | 0.135077862 | 0.067 | 0.201 | |
| Control vs Hot_control | 1 | 0.05266301 | 2.16167342 | 0.063277738 | 0.176 | 0.528 | |
| Treatment vs Hot_control | 1 | -0.00189892 | -0.06088838 | -0.002104017 | 0.854 | 1.000 |
beta_richness_nmds_transplant3 <- beta_div_richness_transplant3$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant3_nmds, by = join_by(sample == newID))
beta_neutral_nmds_transplant3 <- beta_div_neutral_transplant3$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant3_nmds, by = join_by(sample == newID))
beta_phylo_nmds_transplant3 <- beta_div_phylo_transplant3$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant3_nmds, by = join_by(sample == newID))
beta_func_nmds_transplant3 <- beta_div_func_transplant3$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant3_nmds, by = join_by(sample == newID))p0<-beta_richness_nmds_transplant3 %>%
group_by(individual) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
theme_classic() +
theme(legend.position="none")
p1<-beta_neutral_nmds_transplant3 %>%
group_by(individual) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
theme_classic() +
theme(legend.position="none")
p2<-beta_phylo_nmds_transplant3 %>%
group_by(individual) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
theme_classic() +
theme(legend.position="none")
p3<-beta_func_nmds_transplant3 %>%
group_by(individual) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Functional beta diversity") +
theme_classic()+
theme(legend.position="none")
6.3.5.2 Comparison between the different experimental time points (Acclimation vs Transplant samples)
The estimated time for calculating the 5151 pairwise combinations is 32 seconds.
6.3.5.3 Comparison of acclimation samples to transplant samples
transplant7<-transplants_metadata%>%
filter(time_point == "4_Transplant2" | time_point == "1_Acclimation"| time_point == "3_Transplant1")%>%
column_to_rownames("newID")
transplant7_nmds <- transplants_metadata %>%
filter(time_point == "4_Transplant2" | time_point == "1_Acclimation"| time_point == "3_Transplant1")
transplant7_counts<-full_counts %>%
filter(time_point == "4_Transplant2" | time_point == "1_Acclimation"| time_point == "3_Transplant1") %>%
subset(select=-c(315:324)) %>%
column_to_rownames("newID")%>%
subset(select=-c(1))%>%
t() %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric)
transplant7_counts <- transplant7_counts[, !names(transplant7_counts) %in% c("AD45 _ LI1_2nd_2", "AD48 _ LI1_2nd_6")]
identical(sort(colnames(transplant7_counts)),sort(as.character(rownames(transplant7))))[1] TRUE6.3.5.3.1 Number of samples used
[1] 73beta_div_richness_transplant7<-hillpair(data=transplant7_counts, q=0)
beta_div_neutral_transplant7<-hillpair(data=transplant7_counts, q=1)
beta_div_phylo_transplant7<-hillpair(data=transplant7_counts, q=1, tree=genome_tree)
beta_div_func_transplant7<-hillpair(data=transplant7_counts, q=1, dist=dist)#Arrange of metadata dataframe
transplant7_arrange<-transplant7[labels(beta_div_neutral_transplant7$S),]
transplant7_arrange <- transplant7_arrange %>%
mutate(time_point = recode(time_point,
"3_Transplant1" = "Transplant",
"4_Transplant2" = "Transplant"))
transplant7_arrange$type_time <- interaction(transplant7_arrange$type, transplant7_arrange$time_point)6.3.5.3.2 Richness
adonis2(formula=beta_div_richness_transplant7$S ~ Population*time_point+type, data=transplant7[labels(beta_div_richness_transplant7$S),], permutations=999, strata=transplant7$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 6 | 5.309519 | 0.2518733 | 3.703392 | 0.001 |
| Residual | 66 | 15.770599 | 0.7481267 | NA | NA |
| Total | 72 | 21.080119 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_richness_transplant7$S,transplant7_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.36208146 | 1.0521088 | 0.06169963 | 0.322 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.28008774 | 4.6054436 | 0.22350616 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.Transplant | 1 | 0.55038651 | 2.2107376 | 0.08124505 | 0.002 | 0.030 | . |
| Control.1_Acclimation vs Treatment.Transplant | 1 | 1.62289430 | 6.7106689 | 0.25123553 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Hot_control.Transplant | 1 | 1.73215888 | 7.4315069 | 0.25250175 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.36066298 | 5.0871520 | 0.24124415 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.Transplant | 1 | 0.52860586 | 2.1820402 | 0.08027507 | 0.003 | 0.045 | . |
| Treatment.1_Acclimation vs Treatment.Transplant | 1 | 1.76810026 | 7.5736721 | 0.27467042 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.Transplant | 1 | 1.87790626 | 8.3291875 | 0.27462613 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.Transplant | 1 | 1.75314247 | 8.7706781 | 0.25971282 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.Transplant | 1 | 0.27700454 | 1.5346880 | 0.07126586 | 0.070 | 1.000 | |
| Hot_control.1_Acclimation vs Hot_control.Transplant | 1 | 0.26448976 | 1.4916174 | 0.06349573 | 0.105 | 1.000 | |
| Control.Transplant vs Treatment.Transplant | 1 | 2.30884687 | 12.4299510 | 0.30002331 | 0.001 | 0.015 | . |
| Control.Transplant vs Hot_control.Transplant | 1 | 2.50396161 | 13.6713271 | 0.30604256 | 0.001 | 0.015 | . |
| Treatment.Transplant vs Hot_control.Transplant | 1 | 0.01688622 | 0.1023282 | 0.00392027 | 0.997 | 1.000 |
6.3.5.3.3 Neutral
adonis2(formula=beta_div_neutral_transplant7$S ~ Population+time_point*type, data=transplant7[labels(beta_div_neutral_transplant7$S),], permutations=999, strata=transplant7$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 8 | 7.284378 | 0.3492417 | 4.293351 | 0.001 |
| Residual | 64 | 13.573319 | 0.6507583 | NA | NA |
| Total | 72 | 20.857698 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_neutral_transplant7$S,transplant7_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.23160196 | 0.7712905 | 0.045988741 | 0.750 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.40153474 | 5.7562378 | 0.264578733 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.Transplant | 1 | 0.56111203 | 2.5583085 | 0.092832565 | 0.002 | 0.030 | . |
| Control.1_Acclimation vs Treatment.Transplant | 1 | 1.88709838 | 8.3257794 | 0.293929402 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Hot_control.Transplant | 1 | 2.02585000 | 9.2317432 | 0.295588471 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.63477039 | 6.8326887 | 0.299250291 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.Transplant | 1 | 0.61335323 | 2.8313912 | 0.101733730 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Treatment.Transplant | 1 | 2.10939140 | 9.4473664 | 0.320822116 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.Transplant | 1 | 2.24827218 | 10.3907678 | 0.320794118 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.Transplant | 1 | 1.87351542 | 10.3925002 | 0.293635661 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.Transplant | 1 | 0.34276062 | 1.9273510 | 0.087897118 | 0.041 | 0.615 | |
| Hot_control.1_Acclimation vs Hot_control.Transplant | 1 | 0.31638309 | 1.8072337 | 0.075911118 | 0.062 | 0.930 | |
| Control.Transplant vs Treatment.Transplant | 1 | 2.48701901 | 14.0199769 | 0.325894571 | 0.001 | 0.015 | . |
| Control.Transplant vs Hot_control.Transplant | 1 | 2.75304261 | 15.6912860 | 0.336064549 | 0.001 | 0.015 | . |
| Treatment.Transplant vs Hot_control.Transplant | 1 | 0.01764676 | 0.1022118 | 0.003915827 | 0.998 | 1.000 |
6.3.5.3.4 Phylogenetic
adonis2(formula=beta_div_phylo_transplant7$S ~ Population+time_point+type, data=transplant7[labels(beta_div_phylo_transplant7$S),], permutations=999, strata=transplant7$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 4 | 0.7377029 | 0.1879202 | 3.933904 | 0.025 |
| Residual | 68 | 3.1879143 | 0.8120798 | NA | NA |
| Total | 72 | 3.9256172 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_phylo_transplant7$S,transplant7_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.04186923 | 0.43916424 | 0.026714511 | 0.731 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.15609416 | 2.55468892 | 0.137684276 | 0.037 | 0.555 | |
| Control.1_Acclimation vs Control.Transplant | 1 | 0.03888650 | 0.83961027 | 0.032493148 | 0.481 | 1.000 | |
| Control.1_Acclimation vs Treatment.Transplant | 1 | 0.28946588 | 4.58406811 | 0.186464994 | 0.003 | 0.045 | . |
| Control.1_Acclimation vs Hot_control.Transplant | 1 | 0.31864880 | 5.37781508 | 0.196429666 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.23108846 | 4.05218385 | 0.202081922 | 0.003 | 0.045 | . |
| Treatment.1_Acclimation vs Control.Transplant | 1 | 0.11794420 | 2.69844074 | 0.097422117 | 0.046 | 0.690 | |
| Treatment.1_Acclimation vs Treatment.Transplant | 1 | 0.37640156 | 6.28511923 | 0.239113210 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.Transplant | 1 | 0.40433696 | 7.18306079 | 0.246138020 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.Transplant | 1 | 0.11597038 | 5.32063275 | 0.175478948 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.Transplant | 1 | 0.03673004 | 1.13023077 | 0.053488804 | 0.346 | 1.000 | |
| Hot_control.1_Acclimation vs Hot_control.Transplant | 1 | 0.04097680 | 1.30539166 | 0.056012432 | 0.276 | 1.000 | |
| Control.Transplant vs Treatment.Transplant | 1 | 0.21736741 | 7.59281199 | 0.207494630 | 0.001 | 0.015 | . |
| Control.Transplant vs Hot_control.Transplant | 1 | 0.25837791 | 9.19762187 | 0.228810100 | 0.001 | 0.015 | . |
| Treatment.Transplant vs Hot_control.Transplant | 1 | 0.00180330 | 0.04804393 | 0.001844435 | 0.962 | 1.000 |
6.3.5.3.5 Functional
adonis2(formula=beta_div_func_transplant7$S ~ Population+time_point+type, data=transplant7[labels(beta_div_func_transplant7$S),], permutations=999, strata=transplant7$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 4 | 0.5122014 | 0.2427344 | 5.449191 | 0.043 |
| Residual | 68 | 1.5979298 | 0.7572656 | NA | NA |
| Total | 72 | 2.1101312 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_func_transplant7$S,transplant7_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.0905830704 | 1.66462866 | 0.0942351347 | 0.204 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.0861813922 | 1.63467278 | 0.0926965190 | 0.211 | 1.000 | |
| Control.1_Acclimation vs Control.Transplant | 1 | 0.0706284677 | 2.02459114 | 0.0749166241 | 0.196 | 1.000 | |
| Control.1_Acclimation vs Treatment.Transplant | 1 | 0.3227173802 | 7.20965350 | 0.2649667516 | 0.010 | 0.150 | |
| Control.1_Acclimation vs Hot_control.Transplant | 1 | 0.3449345536 | 8.46661273 | 0.2778980651 | 0.007 | 0.105 | |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.0010225901 | 0.05430389 | 0.0033825127 | 0.650 | 1.000 | |
| Treatment.1_Acclimation vs Control.Transplant | 1 | -0.0046542303 | -0.35270812 | -0.0143102181 | 0.811 | 1.000 | |
| Treatment.1_Acclimation vs Treatment.Transplant | 1 | 0.0783171063 | 4.43726923 | 0.1815779491 | 0.077 | 1.000 | |
| Treatment.1_Acclimation vs Hot_control.Transplant | 1 | 0.0836921311 | 5.20043693 | 0.1911894629 | 0.056 | 0.840 | |
| Hot_control.1_Acclimation vs Control.Transplant | 1 | -0.0042700245 | -0.35258632 | -0.0143052054 | 0.809 | 1.000 | |
| Hot_control.1_Acclimation vs Treatment.Transplant | 1 | 0.0824858621 | 5.06251874 | 0.2019956092 | 0.061 | 0.915 | |
| Hot_control.1_Acclimation vs Hot_control.Transplant | 1 | 0.0887346857 | 5.97129920 | 0.2134795084 | 0.045 | 0.675 | |
| Control.Transplant vs Treatment.Transplant | 1 | 0.1927489878 | 15.76935832 | 0.3522355226 | 0.008 | 0.120 | |
| Control.Transplant vs Hot_control.Transplant | 1 | 0.2075592800 | 18.09824701 | 0.3686128958 | 0.005 | 0.075 | |
| Treatment.Transplant vs Hot_control.Transplant | 1 | -0.0001900114 | -0.01304792 | -0.0005020952 | 0.682 | 1.000 |
beta_richness_nmds_transplant7 <- beta_div_richness_transplant7$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant7_nmds, by = join_by(sample == newID))
beta_neutral_nmds_transplant7 <- beta_div_neutral_transplant7$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant7_nmds, by = join_by(sample == newID))
beta_phylo_nmds_transplant7 <- beta_div_phylo_transplant7$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant7_nmds, by = join_by(sample == newID))
beta_func_nmds_transplant7 <- beta_div_func_transplant7$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant7_nmds, by = join_by(sample == newID))p0<-beta_richness_nmds_transplant7 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
scale_shape_manual(name="time_point",
breaks=c("1_Acclimation", "3_Transplant1", "4_Transplant2"),
labels=c("Acclimation", "Transplant", "Transplant"),
values=c("circle","square","square")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
theme_classic() +
theme(legend.position="none")
p1<-beta_neutral_nmds_transplant7 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
scale_shape_manual(name="time_point",
breaks=c("1_Acclimation", "3_Transplant1", "4_Transplant2"),
labels=c("Acclimation", "Transplant", "Transplant"),
values=c("circle","square","square")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
theme_classic() +
theme(legend.position="none")
p2<-beta_phylo_nmds_transplant7 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
scale_shape_manual(name="time_point",
breaks=c("1_Acclimation", "3_Transplant1", "4_Transplant2"),
labels=c("Acclimation", "Transplant", "Transplant"),
values=c("circle","square","square")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
theme_classic() +
theme(legend.position="none")
p3<-beta_func_nmds_transplant7 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
scale_shape_manual(name="time_point",
breaks=c("1_Acclimation", "3_Transplant1", "4_Transplant2"),
labels=c("Acclimation", "Transplant", "Transplant"),
values=c("circle","square","square")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Functional beta diversity") +
theme_classic()+
theme(legend.position="none")
6.3.5.4 Comparison between Acclimation vs Post-FMT1
post3 <- meta %>%
filter(time_point == "1_Acclimation" | time_point == "5_Post-FMT1")
temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL
post3.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(post3))]
identical(sort(colnames(post3.counts)),sort(as.character(rownames(post3))))
post3_nmds <- sample_metadata %>%
filter(time_point == "1_Acclimation" | time_point == "5_Post-FMT1")6.3.5.4.1 Number of samples used
[1] 536.3.5.4.2 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.099607 0.049803 9.5441 999 0.001 ***
Residuals 50 0.260911 0.005218
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.00100000 0.876
Hot_control 0.00102653 0.001
Treatment 0.88832670 0.00010131 adonis2(formula=beta_div_richness_post3$S ~ time_point*type, data=post3[labels(beta_div_neutral_post3$S),], permutations=999,strata=post3$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 5 | 4.403362 | 0.2369995 | 2.919782 | 0.001 |
| Residual | 47 | 14.176268 | 0.7630005 | NA | NA |
| Total | 52 | 18.579631 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(beta_div_richness_post3$S, post3_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.3620815 | 1.052109 | 0.06169963 | 0.347 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.2800877 | 4.605444 | 0.22350616 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.6845657 | 1.998114 | 0.11101796 | 0.002 | 0.030 | . |
| Control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.8437461 | 2.499232 | 0.14281954 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.1208022 | 3.568670 | 0.18236649 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.3606630 | 5.087152 | 0.24124415 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.7216200 | 2.172734 | 0.11956009 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.9551308 | 2.926054 | 0.16322910 | 0.002 | 0.030 | . |
| Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.2263345 | 4.039487 | 0.20157637 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 1.4319792 | 5.384836 | 0.25180628 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.8172413 | 3.194690 | 0.17558364 | 0.002 | 0.030 | . |
| Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.5796135 | 2.441615 | 0.13239702 | 0.001 | 0.015 | . |
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.5615418 | 1.729004 | 0.10335366 | 0.013 | 0.195 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.8438429 | 2.793772 | 0.14865413 | 0.001 | 0.015 | . |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.3734921 | 1.268929 | 0.07799710 | 0.133 | 1.000 |
6.3.5.4.3 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.00945 0.0094472 1.1428 999 0.31
Residuals 51 0.42161 0.0082669
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 5_Post-FMT1
1_Acclimation 0.311
5_Post-FMT1 0.2901 adonis2(formula=beta_div_neutral_post3$S ~ time_point*type, data=post3[labels(beta_div_neutral_post3$S),], permutations=999,strata=post3$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 5 | 5.302354 | 0.3027004 | 4.080576 | 0.001 |
| Residual | 47 | 12.214484 | 0.6972996 | NA | NA |
| Total | 52 | 17.516838 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(beta_div_neutral_post3$S, post3_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.2316020 | 0.7712905 | 0.04598874 | 0.727 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.4015347 | 5.7562378 | 0.26457873 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.8332162 | 2.9081103 | 0.15380227 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 1.1719595 | 4.0685514 | 0.21336447 | 0.002 | 0.030 | . |
| Control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.4260875 | 5.2413171 | 0.24675104 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.6347704 | 6.8326887 | 0.29925029 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.9517634 | 3.3715700 | 0.17404733 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 1.3127773 | 4.6298256 | 0.23585668 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.6713369 | 6.2395460 | 0.28056085 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 1.5409781 | 6.8338056 | 0.29928456 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.9133614 | 4.0964534 | 0.21451383 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.6954835 | 3.2951234 | 0.17077493 | 0.001 | 0.015 | . |
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.6051778 | 2.2508491 | 0.13047758 | 0.015 | 0.225 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 1.0528902 | 4.1436369 | 0.20570451 | 0.001 | 0.015 | . |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.4150076 | 1.6372683 | 0.09840968 | 0.056 | 0.840 |
6.3.5.4.4 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.05132 0.051320 2.6745 999 0.119
Residuals 51 0.97861 0.019189
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 5_Post-FMT1
1_Acclimation 0.11
5_Post-FMT1 0.10812 adonis2(formula=beta_div_phylo_post3$S ~ time_point*type, data=post3[labels(beta_div_phylo_post3$S),], permutations=999,strata=post3$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 5 | 0.7935087 | 0.2278753 | 2.774199 | 0.006 |
| Residual | 47 | 2.6886978 | 0.7721247 | NA | NA |
| Total | 52 | 3.4822065 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(beta_div_phylo_post3$S, post3_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.04186923 | 0.4391642 | 0.02671451 | 0.737 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.15609416 | 2.5546889 | 0.13768428 | 0.030 | 0.450 | |
| Control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.19193367 | 2.9749922 | 0.15678490 | 0.025 | 0.375 | |
| Control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.14627288 | 1.7907381 | 0.10665035 | 0.150 | 1.000 | |
| Control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.25061348 | 3.6146185 | 0.18428187 | 0.009 | 0.135 | |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.23108846 | 4.0521838 | 0.20208192 | 0.003 | 0.045 | . |
| Treatment.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.26358465 | 4.3608960 | 0.21417997 | 0.006 | 0.090 | |
| Treatment.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.25319427 | 3.2738422 | 0.17915456 | 0.034 | 0.510 | |
| Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.39050120 | 5.9837393 | 0.27218933 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.14203376 | 5.4200212 | 0.25303529 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.09666753 | 2.3682173 | 0.13635351 | 0.013 | 0.195 | |
| Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.09252600 | 2.9824958 | 0.15711821 | 0.005 | 0.075 | |
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.01842535 | 0.4144162 | 0.02688498 | 0.766 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.05987967 | 1.7387847 | 0.09802164 | 0.109 | 1.000 | |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.03212966 | 0.6477782 | 0.04139746 | 0.690 | 1.000 |
6.3.5.4.5 Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.00554 0.0055401 0.2063 999 0.63
Residuals 51 1.36938 0.0268505
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 5_Post-FMT1
1_Acclimation 0.641
5_Post-FMT1 0.65159 adonis2(formula=beta_div_func_post3$S ~ time_point*type, data=post3[labels(beta_div_func_post3$S),], permutations=999,strata=post3$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 5 | 0.2935075 | 0.1510442 | 1.672425 | 0.054 |
| Residual | 47 | 1.6496826 | 0.8489558 | NA | NA |
| Total | 52 | 1.9431901 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(beta_div_func_post3$S, post3_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.090583070 | 1.66462866 | 0.094235135 | 0.200 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.086181392 | 1.63467278 | 0.092696519 | 0.256 | 1.000 | |
| Control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.028641941 | 0.50417680 | 0.030548437 | 0.527 | 1.000 | |
| Control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.234795406 | 4.03037749 | 0.211786524 | 0.059 | 0.885 | |
| Control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.134726259 | 2.20299547 | 0.121023788 | 0.158 | 1.000 | |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.001022590 | 0.05430389 | 0.003382513 | 0.667 | 1.000 | |
| Treatment.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.002157067 | 0.09411569 | 0.005847832 | 0.607 | 1.000 | |
| Treatment.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.056602363 | 2.56037069 | 0.145803909 | 0.159 | 1.000 | |
| Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.009569124 | 0.35095521 | 0.021463896 | 0.511 | 1.000 | |
| Hot_control.1_Acclimation vs Control.5_Post-FMT1 | 1 | -0.001745663 | -0.08225018 | -0.005167199 | 0.730 | 1.000 | |
| Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.057758674 | 2.84545622 | 0.159449901 | 0.164 | 1.000 | |
| Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.005575266 | 0.21803560 | 0.013444020 | 0.540 | 1.000 | |
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.119540855 | 4.84764704 | 0.244242909 | 0.068 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.052587837 | 1.77308932 | 0.099762584 | 0.216 | 1.000 | |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.012980354 | 0.44307662 | 0.028690955 | 0.486 | 1.000 |
beta_richness_nmds_post3 <- beta_div_richness_post3$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post3_nmds, by = c("sample" = "Tube_code"))
beta_neutral_nmds_post3 <- beta_div_neutral_post3$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post3_nmds, by = c("sample" = "Tube_code"))
beta_phylo_nmds_post3 <- beta_div_phylo_post3$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post3_nmds, by = join_by(sample == Tube_code))
beta_func_nmds_post3 <- beta_div_func_post3$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post3_nmds, by = join_by(sample == Tube_code))
6.3.5.5 Comparison between Acclimation vs Post-FMT2
post4 <- meta %>%
filter(time_point == "1_Acclimation" | time_point == "6_Post-FMT2")
temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL
post4.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(post4))]
identical(sort(colnames(post4.counts)),sort(as.character(rownames(post4))))
post4_nmds <- sample_metadata %>%
filter(time_point == "1_Acclimation" | time_point == "6_Post-FMT2")6.3.5.5.1 Number of samples used
[1] 546.3.5.5.2 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.06809 0.034047 3.8471 999 0.023 *
Residuals 51 0.45135 0.008850
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.0270000 0.902
Hot_control 0.0349385 0.002
Treatment 0.8855174 0.0047257 adonis2(formula=beta_div_richness_post4$S ~ time_point*Population, data=post4[labels(beta_div_neutral_post4$S),], permutations=999,strata=post4$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 3.310172 | 0.1883377 | 3.867324 | 0.001 |
| Residual | 50 | 14.265560 | 0.8116623 | NA | NA |
| Total | 53 | 17.575732 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(beta_div_richness_post4$S, post4_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.3620815 | 1.052109 | 0.06169963 | 0.323 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.2800877 | 4.605444 | 0.22350616 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.8430295 | 2.845779 | 0.15100353 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.5232174 | 1.683240 | 0.09518843 | 0.031 | 0.465 | |
| Control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.1217138 | 3.634271 | 0.18509835 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.3606630 | 5.087152 | 0.24124415 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.9130048 | 3.195028 | 0.16645080 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.5959230 | 1.984036 | 0.11032208 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.2747787 | 4.275366 | 0.21086503 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.6397330 | 2.913695 | 0.15405213 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 1.4575447 | 6.224524 | 0.28007456 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.3276169 | 1.412318 | 0.08111028 | 0.042 | 0.630 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 0.6463814 | 2.560441 | 0.13795154 | 0.001 | 0.015 | . |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.4796256 | 1.916520 | 0.10696943 | 0.002 | 0.030 | . |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 1.1305044 | 4.268317 | 0.21059061 | 0.001 | 0.015 | . |
6.3.5.5.3 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.01544 0.0154447 2.0972 999 0.146
Residuals 52 0.38294 0.0073643
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 6_Post-FMT2
1_Acclimation 0.152
6_Post-FMT2 0.15357 adonis2(formula=beta_div_neutral_post4$S ~ time_point*Population, data=post4[labels(beta_div_neutral_post4$S),], permutations=999,strata=post4$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 3.863228 | 0.229321 | 4.959284 | 0.001 |
| Residual | 50 | 12.983151 | 0.770679 | NA | NA |
| Total | 53 | 16.846379 | 1.000000 | NA | NA |
pairwise <- pairwise.adonis(beta_div_neutral_post4$S, post4_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.2316020 | 0.7712905 | 0.04598874 | 0.738 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.4015347 | 5.7562378 | 0.26457873 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 1.1746426 | 4.5564741 | 0.22165640 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.5286441 | 1.9819408 | 0.11021840 | 0.002 | 0.030 | . |
| Control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.3443224 | 4.9104417 | 0.23483204 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.6347704 | 6.8326887 | 0.29925029 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 1.3540292 | 5.3398081 | 0.25022756 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.6311089 | 2.4041625 | 0.13063146 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.6125755 | 5.9825981 | 0.27215155 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.6202327 | 3.1519868 | 0.16457754 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 1.5701179 | 7.6327037 | 0.32297209 | 0.002 | 0.030 | . |
| Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.3634438 | 1.7083388 | 0.09647087 | 0.041 | 0.615 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 1.0227481 | 4.6483346 | 0.22511910 | 0.001 | 0.015 | . |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.5010202 | 2.2065321 | 0.12119453 | 0.001 | 0.015 | . |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 1.3619424 | 5.7710313 | 0.26507845 | 0.001 | 0.015 | . |
6.3.5.5.4 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.06978 0.069777 5.0345 999 0.031 *
Residuals 52 0.72071 0.013860
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 6_Post-FMT2
1_Acclimation 0.031
6_Post-FMT2 0.029131 adonis2(formula=beta_div_phylo_post4$S ~ time_point*Population, data=post4[labels(beta_div_phylo_post4$S),], permutations=999,strata=post4$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 0.757493 | 0.2376349 | 5.195124 | 0.001 |
| Residual | 50 | 2.430141 | 0.7623651 | NA | NA |
| Total | 53 | 3.187634 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(beta_div_phylo_post4$S, post4_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.04186923 | 0.4391642 | 0.02671451 | 0.763 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.15609416 | 2.5546889 | 0.13768428 | 0.042 | 0.630 | |
| Control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.26322331 | 4.3060281 | 0.21205664 | 0.004 | 0.060 | |
| Control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.16047895 | 2.5405742 | 0.13702781 | 0.041 | 0.615 | |
| Control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.25529510 | 4.0109138 | 0.20043631 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.23108846 | 4.0521838 | 0.20208192 | 0.006 | 0.090 | |
| Treatment.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.36496892 | 6.3966666 | 0.28560797 | 0.002 | 0.030 | . |
| Treatment.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.22628210 | 3.8292220 | 0.19311005 | 0.017 | 0.255 | |
| Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.34830814 | 5.8463335 | 0.26761166 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.10002871 | 4.3836237 | 0.21505615 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.12577510 | 5.0601287 | 0.24027055 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.06334378 | 2.4997737 | 0.13512455 | 0.018 | 0.270 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 0.05927454 | 2.3820253 | 0.12958449 | 0.025 | 0.375 | |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.06906280 | 2.7224602 | 0.14541146 | 0.005 | 0.075 | |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.11081709 | 4.0436561 | 0.20174244 | 0.002 | 0.030 | . |
6.3.5.5.5 Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.00527 0.005269 0.1889 999 0.696
Residuals 52 1.45058 0.027896
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 6_Post-FMT2
1_Acclimation 0.69
6_Post-FMT2 0.66565 adonis2(formula=beta_div_func_post4$S ~ time_point*Population, data=post4[labels(beta_div_func_post4$S),], permutations=999,strata=post4$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 0.0773959 | 0.0417692 | 0.726498 | 0.298 |
| Residual | 50 | 1.7755477 | 0.9582308 | NA | NA |
| Total | 53 | 1.8529436 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(beta_div_func_post4$S, post4_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.0905830704 | 1.664628661 | 0.0942351347 | 0.237 | 1 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.0861813922 | 1.634672780 | 0.0926965190 | 0.260 | 1 | |
| Control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.1197900330 | 2.213130846 | 0.1215129274 | 0.168 | 1 | |
| Control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.1125623700 | 2.150784454 | 0.1184953995 | 0.197 | 1 | |
| Control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.0657004998 | 0.954588109 | 0.0563026423 | 0.289 | 1 | |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.0010225901 | 0.054303886 | 0.0033825127 | 0.687 | 1 | |
| Treatment.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | -0.0005177706 | -0.025585400 | -0.0016016487 | 0.720 | 1 | |
| Treatment.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.0013301207 | 0.072110871 | 0.0044867082 | 0.609 | 1 | |
| Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.0060959077 | 0.174487757 | 0.0107878382 | 0.601 | 1 | |
| Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.0010345754 | 0.055797964 | 0.0034752533 | 0.654 | 1 | |
| Hot_control.1_Acclimation vs Control.6_Post-FMT2 | 1 | -0.0001056284 | -0.006306177 | -0.0003942915 | 0.690 | 1 | |
| Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.0017235602 | 0.051851181 | 0.0032302306 | 0.755 | 1 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | -0.0080428882 | -0.442986255 | -0.0284750185 | 0.850 | 1 | |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | -0.0011796256 | -0.034047378 | -0.0021324990 | 0.914 | 1 | |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.0036300838 | 0.110487573 | 0.0068581148 | 0.710 | 1 |
beta_richness_nmds_post4 <- beta_div_richness_post4$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post4_nmds, by = c("sample" = "Tube_code"))
beta_neutral_nmds_post4 <- beta_div_neutral_post4$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post4_nmds, by = c("sample" = "Tube_code"))
beta_phylo_nmds_post4 <- beta_div_phylo_post4$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post4_nmds, by = join_by(sample == Tube_code))
beta_func_nmds_post4 <- beta_div_func_post4$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post4_nmds, by = join_by(sample == Tube_code))
6.3.6 6. Are there differences between the control and the treatment group?
6.3.6.1 After 1 week –> Post-FMT1
post1 <- meta %>%
filter(time_point == "5_Post-FMT1")
post1.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(post1))]
identical(sort(colnames(post1.counts)),sort(as.character(rownames(post1))))
post1_nmds <- sample_metadata %>%
filter(time_point == "5_Post-FMT1")6.3.6.1.1 Number of samples used
[1] 266.3.6.1.2 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.017675 0.0088373 2.3825 999 0.094 .
Residuals 23 0.085312 0.0037092
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.0040000 0.663
Hot_control 0.0068795 0.210
Treatment 0.6248469 0.2084296 adonis2(formula=beta_div_richness_post1$S ~ Population+type, data=post1[labels(beta_div_richness_post1$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 2 | 1.195567 | 0.1448246 | 1.947534 | 0.001 |
| Residual | 23 | 7.059710 | 0.8551754 | NA | NA |
| Total | 25 | 8.255277 | 1.0000000 | NA | NA |
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Control vs Treatment 1 0.5615418 1.729004 0.1033537 0.015 0.045 .
2 Control vs Hot_control 1 0.8438429 2.793772 0.1486541 0.001 0.003 *
3 Treatment vs Hot_control 1 0.3734921 1.268929 0.0779971 0.107 0.321 6.3.6.1.3 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.011001 0.0055005 0.6303 999 0.566
Residuals 23 0.200714 0.0087267
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.20100 0.962
Hot_control 0.21166 0.449
Treatment 0.95468 0.43604 adonis2(formula=beta_div_neutral_post1$S ~ Population+type, data=post1[labels(beta_div_neutral_post1$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 2 | 1.395968 | 0.1900228 | 2.697931 | 0.001 |
| Residual | 23 | 5.950350 | 0.8099772 | NA | NA |
| Total | 25 | 7.346318 | 1.0000000 | NA | NA |
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Control vs Treatment 1 0.6051778 2.250849 0.13047758 0.013 0.039 .
2 Control vs Hot_control 1 1.0528902 4.143637 0.20570451 0.001 0.003 *
3 Treatment vs Hot_control 1 0.4150076 1.637268 0.09840968 0.053 0.159 6.3.6.1.4 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.00440 0.0021994 0.1369 999 0.887
Residuals 23 0.36941 0.0160614
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.92700 0.664
Hot_control 0.91505 0.745
Treatment 0.63312 0.73046 adonis2(formula=beta_div_phylo_post1$S ~ Population+type, data=post1[labels(beta_div_phylo_post1$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 2 | 0.0745104 | 0.0705947 | 0.8735033 | 0.547 |
| Residual | 23 | 0.9809570 | 0.9294053 | NA | NA |
| Total | 25 | 1.0554673 | 1.0000000 | NA | NA |
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Control vs Treatment 1 0.01842535 0.4144162 0.02688498 0.756 1.000
2 Control vs Hot_control 1 0.05987967 1.7387847 0.09802164 0.108 0.324
3 Treatment vs Hot_control 1 0.03212966 0.6477782 0.04139746 0.702 1.000 6.3.6.1.5 Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.00400 0.0019999 0.1431 999 0.859
Residuals 23 0.32135 0.0139717
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.60900 0.734
Hot_control 0.60188 0.830
Treatment 0.74597 0.84473 adonis2(formula=beta_div_func_post1$S ~ Population+type, data=post1[labels(beta_div_func_post1$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 2 | 0.1230554 | 0.1608583 | 2.204479 | 0.174 |
| Residual | 23 | 0.6419374 | 0.8391417 | NA | NA |
| Total | 25 | 0.7649929 | 1.0000000 | NA | NA |
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Control vs Treatment 1 0.11954085 4.8476470 0.24424291 0.060 0.180
2 Control vs Hot_control 1 0.05258784 1.7730893 0.09976258 0.246 0.738
3 Treatment vs Hot_control 1 0.01298035 0.4430766 0.02869096 0.469 1.000 beta_richness_nmds_post1 <- beta_div_richness_post1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post1_nmds, by = join_by(sample == Tube_code))
beta_neutral_nmds_post1 <- beta_div_neutral_post1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post1_nmds, by = join_by(sample == Tube_code))
beta_phylogenetic_nmds_post1 <- beta_div_phylo_post1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post1_nmds, by = join_by(sample == Tube_code))
beta_functional_nmds_post1 <- beta_div_func_post1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post1_nmds, by = join_by(sample == Tube_code))p0<-beta_richness_nmds_post1 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
theme_classic() +
theme(legend.position="none")
p1<-beta_neutral_nmds_post1 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
theme_classic() +
theme(legend.position="none")
p2<-beta_phylogenetic_nmds_post1 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
theme_classic() +
theme(legend.position="none")
p3<-beta_functional_nmds_post1 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Functional beta diversity") +
theme_classic()+
theme(legend.position="none")
6.3.6.2 After 2 weeks –>Post-FMT2
post2 <- meta %>%
filter(time_point == "6_Post-FMT2")
post2.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(post2))]
identical(sort(colnames(post2.counts)),sort(as.character(rownames(post2))))
post2_nmds <- sample_metadata %>%
filter(time_point == "6_Post-FMT2")6.3.6.2.1 Number of samples used
[1] 276.3.6.2.2 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.002011 0.0010056 0.1982 999 0.826
Residuals 24 0.121775 0.0050740
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.71100 0.795
Hot_control 0.67789 0.602
Treatment 0.79246 0.59820 adonis2(formula=beta_div_richness_post2$S ~ type, data=post2[labels(beta_div_richness_post2$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 2 | 1.504341 | 0.1967776 | 2.939822 | 0.001 |
| Residual | 24 | 6.140538 | 0.8032224 | NA | NA |
| Total | 26 | 7.644879 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Treatment vs Control | 1 | 0.6463814 | 2.560441 | 0.1379515 | 0.001 | 0.003 | * |
| Treatment vs Hot_control | 1 | 0.4796256 | 1.916520 | 0.1069694 | 0.001 | 0.003 | * |
| Control vs Hot_control | 1 | 1.1305044 | 4.268317 | 0.2105906 | 0.001 | 0.003 | * |
6.3.6.2.3 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.008262 0.0041311 0.8024 999 0.458
Residuals 24 0.123559 0.0051483
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.44800 0.666
Hot_control 0.44675 0.237
Treatment 0.65989 0.25095 adonis2(formula=beta_div_neutral_post2$S ~ type, data=post2[labels(beta_div_neutral_post2$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 2 | 1.923807 | 0.2603795 | 4.224537 | 0.001 |
| Residual | 24 | 5.464666 | 0.7396205 | NA | NA |
| Total | 26 | 7.388473 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Treatment vs Control | 1 | 1.0227481 | 4.648335 | 0.2251191 | 0.001 | 0.003 | * |
| Treatment vs Hot_control | 1 | 0.5010202 | 2.206532 | 0.1211945 | 0.001 | 0.003 | * |
| Control vs Hot_control | 1 | 1.3619424 | 5.771031 | 0.2650785 | 0.001 | 0.003 | * |
6.3.6.2.4 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.000407 0.0002034 0.0487 999 0.952
Residuals 24 0.100305 0.0041794
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.94500 0.853
Hot_control 0.93765 0.751
Treatment 0.83933 0.76015 adonis2(formula=beta_div_phylo_post2$S ~ type, data=post2[labels(beta_div_phylo_post2$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 2 | 0.1594363 | 0.2042241 | 3.079623 | 0.001 |
| Residual | 24 | 0.6212564 | 0.7957759 | NA | NA |
| Total | 26 | 0.7806927 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Treatment vs Control | 1 | 0.05927454 | 2.382025 | 0.1295845 | 0.022 | 0.066 | |
| Treatment vs Hot_control | 1 | 0.06906280 | 2.722460 | 0.1454115 | 0.003 | 0.009 | * |
| Control vs Hot_control | 1 | 0.11081709 | 4.043656 | 0.2017424 | 0.002 | 0.006 | * |
6.3.6.2.5 Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.01259 0.0062962 0.3249 999 0.773
Residuals 24 0.46507 0.0193778
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.50800 0.66
Hot_control 0.45381 0.79
Treatment 0.57452 0.74365 adonis2(formula=beta_div_func_post2$S ~ type, data=post2[labels(beta_div_func_post2$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 2 | -0.0037283 | -0.0054704 | -0.065288 | 0.913 |
| Residual | 24 | 0.6852623 | 1.0054704 | NA | NA |
| Total | 26 | 0.6815340 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Treatment vs Control | 1 | -0.008042888 | -0.44298625 | -0.028475019 | 0.858 | 1 | |
| Treatment vs Hot_control | 1 | -0.001179626 | -0.03404738 | -0.002132499 | 0.889 | 1 | |
| Control vs Hot_control | 1 | 0.003630084 | 0.11048757 | 0.006858115 | 0.674 | 1 |
beta_richness_nmds_post2 <- beta_div_richness_post2$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post2_nmds, by = join_by(sample == Tube_code))
beta_neutral_nmds_post2 <- beta_div_neutral_post2$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post2_nmds, by = join_by(sample == Tube_code))
beta_phylogenetic_nmds_post2 <- beta_div_phylo_post2$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post2_nmds, by = join_by(sample == Tube_code))
beta_functional_nmds_post2 <- beta_div_func_post2$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post2_nmds, by = join_by(sample == Tube_code))p0<-beta_richness_nmds_post2 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
theme_classic() +
theme(legend.position="none")
p1<-beta_neutral_nmds_post2 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
theme_classic() +
theme(legend.position="none")
p2<-beta_phylogenetic_nmds_post2 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
theme_classic() +
theme(legend.position="none")
p3<-beta_functional_nmds_post2 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Functional beta diversity") +
theme_classic()+
theme(legend.position="none")
6.3.6.3 Post1 vs Post2
post5 <- meta %>%
filter(time_point == "6_Post-FMT2" | time_point == "5_Post-FMT1")
post5.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(post5))]
identical(sort(colnames(post5.counts)),sort(as.character(rownames(post5))))
post5_nmds <- sample_metadata %>%
filter(time_point == "6_Post-FMT2"| time_point == "5_Post-FMT1")6.3.6.3.1 Number of samples used
[1] 536.3.6.3.2 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.01841 0.0092048 1.7364 999 0.199
Residuals 50 0.26505 0.0053010
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.043000 0.731
Hot_control 0.039117 0.225
Treatment 0.716358 0.218648 adonis2(formula=beta_div_richness_post5$S ~ type, data=post5[labels(beta_div_richness_post5$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 2 | 2.28826 | 0.1390012 | 4.036044 | 0.001 |
| Residual | 50 | 14.17390 | 0.8609988 | NA | NA |
| Total | 52 | 16.46216 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_richness_post5$S,post5_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.5615418 | 1.729004 | 0.10335366 | 0.018 | 0.270 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.8438429 | 2.793772 | 0.14865413 | 0.001 | 0.015 | . |
| Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.7628135 | 2.683925 | 0.14364890 | 0.001 | 0.015 | . |
| Control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.3432605 | 1.148733 | 0.06698647 | 0.255 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 1.1269580 | 3.799256 | 0.19188884 | 0.001 | 0.015 | . |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.3734921 | 1.268929 | 0.07799710 | 0.116 | 1.000 | |
| Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.3571397 | 1.297184 | 0.07959561 | 0.119 | 1.000 | |
| Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.7769467 | 2.670898 | 0.15114670 | 0.002 | 0.030 | . |
| Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.6502360 | 2.253407 | 0.13060650 | 0.001 | 0.015 | . |
| Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.4132091 | 1.616138 | 0.09174188 | 0.008 | 0.120 | |
| Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 1.0163992 | 3.760571 | 0.19030682 | 0.001 | 0.015 | . |
| Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.2732563 | 1.019281 | 0.05988979 | 0.386 | 1.000 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 0.6463814 | 2.560441 | 0.13795154 | 0.001 | 0.015 | . |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.4796256 | 1.916520 | 0.10696943 | 0.002 | 0.030 | . |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 1.1305044 | 4.268317 | 0.21059061 | 0.001 | 0.015 | . |
6.3.6.3.3 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.01992 0.0099587 1.565 999 0.219
Residuals 50 0.31818 0.0063636
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.11300 0.871
Hot_control 0.10701 0.167
Treatment 0.87156 0.17449 adonis2(formula=beta_div_neutral_post5$S ~ type, data=post5[labels(beta_div_neutral_post5$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 2 | 2.928527 | 0.1906221 | 5.887921 | 0.001 |
| Residual | 50 | 12.434468 | 0.8093779 | NA | NA |
| Total | 52 | 15.362995 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_neutral_post5$S,post5_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.6051778 | 2.250849 | 0.13047758 | 0.016 | 0.240 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 1.0528902 | 4.143637 | 0.20570451 | 0.001 | 0.015 | . |
| Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.8908158 | 3.714692 | 0.18842252 | 0.001 | 0.015 | . |
| Control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.3860927 | 1.552176 | 0.08843210 | 0.077 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 1.3122237 | 5.130273 | 0.24279254 | 0.001 | 0.015 | . |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.4150076 | 1.637268 | 0.09840968 | 0.048 | 0.720 | |
| Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.3157079 | 1.325203 | 0.08117526 | 0.150 | 1.000 | |
| Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 1.0579520 | 4.270010 | 0.22158835 | 0.001 | 0.015 | . |
| Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.7454015 | 2.920049 | 0.16294873 | 0.004 | 0.060 | |
| Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.4377161 | 1.942126 | 0.10824392 | 0.005 | 0.075 | |
| Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 1.3766597 | 5.875279 | 0.26858075 | 0.001 | 0.015 | . |
| Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.3176516 | 1.316137 | 0.07600637 | 0.198 | 1.000 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 1.0227481 | 4.648335 | 0.22511910 | 0.001 | 0.015 | . |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.5010202 | 2.206532 | 0.12119453 | 0.002 | 0.030 | . |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 1.3619424 | 5.771031 | 0.26507845 | 0.001 | 0.015 | . |
6.3.6.3.4 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.00051 0.0002543 0.0265 999 0.983
Residuals 50 0.47996 0.0095993
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.90200 0.85
Hot_control 0.88926 0.92
Treatment 0.82391 0.91902 adonis2(formula=beta_div_phylo_post5$S ~ type, data=post5[labels(beta_div_phylo_post5$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 2 | 0.1778594 | 0.0910216 | 2.503403 | 0.005 |
| Residual | 50 | 1.7761762 | 0.9089784 | NA | NA |
| Total | 52 | 1.9540356 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_phylo_post5$S,post5_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.01842535 | 0.4144162 | 0.02688498 | 0.796 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.05987967 | 1.7387847 | 0.09802164 | 0.131 | 1.000 | |
| Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.07917244 | 3.0180046 | 0.15869197 | 0.007 | 0.105 | |
| Control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.04335491 | 1.5335604 | 0.08746429 | 0.197 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.10783045 | 3.7500438 | 0.18987521 | 0.001 | 0.015 | . |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.03212966 | 0.6477782 | 0.04139746 | 0.679 | 1.000 | |
| Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.06393539 | 1.5651817 | 0.09448624 | 0.147 | 1.000 | |
| Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.05265949 | 1.2240203 | 0.07544494 | 0.304 | 1.000 | |
| Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.09753501 | 2.2402429 | 0.12994265 | 0.013 | 0.195 | |
| Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.07228545 | 2.3279593 | 0.12701683 | 0.038 | 0.570 | |
| Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.11759094 | 3.5538444 | 0.18174658 | 0.001 | 0.015 | . |
| Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.06667255 | 1.9859527 | 0.11041687 | 0.113 | 1.000 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 0.05927454 | 2.3820253 | 0.12958449 | 0.040 | 0.600 | |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.06906280 | 2.7224602 | 0.14541146 | 0.002 | 0.030 | . |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.11081709 | 4.0436561 | 0.20174244 | 0.002 | 0.030 | . |
6.3.6.3.5 Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.00785 0.0039232 0.2322 999 0.826
Residuals 50 0.84483 0.0168966
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.56500 0.585
Hot_control 0.52384 0.865
Treatment 0.58787 0.85068 adonis2(formula=beta_div_func_post5$S ~ type, data=post5[labels(beta_div_func_post5$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 2 | 0.0668661 | 0.0466009 | 1.221967 | 0.308 |
| Residual | 50 | 1.3680018 | 0.9533991 | NA | NA |
| Total | 52 | 1.4348679 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.1195408549 | 4.84764704 | 0.2442429086 | 0.068 | 1 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.0525878365 | 1.77308932 | 0.0997625840 | 0.212 | 1 | |
| Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.0265995825 | 1.17541806 | 0.0684360667 | 0.319 | 1 | |
| Control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.0145818992 | 0.69975992 | 0.0419023938 | 0.385 | 1 | |
| Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | -0.0080695208 | -0.21617323 | -0.0136958691 | 0.932 | 1 | |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.0129803540 | 0.44307662 | 0.0286909552 | 0.489 | 1 | |
| Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.0267162134 | 1.22560581 | 0.0755352882 | 0.299 | 1 | |
| Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.0384388433 | 1.93281582 | 0.1141461550 | 0.246 | 1 | |
| Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.0553988290 | 1.47819391 | 0.0897060633 | 0.247 | 1 | |
| Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | -0.0040061386 | -0.14850469 | -0.0093684974 | 0.753 | 1 | |
| Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.0024023972 | 0.09538980 | 0.0059265296 | 0.620 | 1 | |
| Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | -0.0004960759 | -0.01190328 | -0.0007445087 | 0.838 | 1 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | -0.0080428882 | -0.44298625 | -0.0284750185 | 0.857 | 1 | |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | -0.0011796256 | -0.03404738 | -0.0021324990 | 0.889 | 1 | |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.0036300838 | 0.11048757 | 0.0068581148 | 0.711 | 1 |
beta_richness_nmds_post5 <- beta_div_richness_post5$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post5_nmds, by = join_by(sample == Tube_code))
beta_neutral_nmds_post5 <- beta_div_neutral_post5$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post5_nmds, by = join_by(sample == Tube_code))
beta_phylogenetic_nmds_post5 <- beta_div_phylo_post5$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post5_nmds, by = join_by(sample == Tube_code))
beta_functional_nmds_post5 <- beta_div_func_post5$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post5_nmds, by = join_by(sample == Tube_code))p0<-beta_richness_nmds_post5 %>%
group_by(type, time_point) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
theme_classic() +
theme(legend.position="none")
p1<-beta_neutral_nmds_post5 %>%
group_by(type, time_point) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
theme_classic() +
theme(legend.position="none")
p2<-beta_phylogenetic_nmds_post5 %>%
group_by(type, time_point) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
theme_classic() +
theme(legend.position="none")
p3<-beta_functional_nmds_post5 %>%
group_by(type, time_point) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Functional beta diversity") +
theme_classic()+
theme(legend.position="none")